Evidence map›Paper›PMID 35273205›Full record

ArticleScientific reports2022

Plant stem tissue modeling and parameter identification using metaheuristic optimization algorithms.

Mohamed S Ghoneim, Samar I Gadallah, Lobna A Said, Ahmed M Eltawil, Ahmed G Radwan, Ahmed H Madian

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In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Plant Tissue Modelling Using Power-Law Filters.Sensors (Basel, Switzerland) · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mohamed S GhoneimNanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt. mohamed_ghoneim@ieee.org.
Samar I GadallahNanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt.
Lobna A SaidNanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt.
Ahmed M EltawilElectrical Engineering and Computer Science Department, University of California-Irvine, Irvine, USA.
Ahmed G RadwanEngineering Mathematics and Physics Department, Cairo University, Giza, Egypt.
Ahmed H MadianNanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bio-impedance non-invasive measurement techniques usage is rapidly increasing in the agriculture industry. These measured impedance variations reflect tacit biochemical and biophysical changes of living and non-living tissues. Bio-impedance circuit modeling is an effective solution used in biology and medicine to fit the measured impedance. This paper proposes two new fractional-order bio-impedance plant stem models. These new models are compared with three commonly used bio-impedance fractional-order circuit models in plant modeling (Cole, Double Cole, and Fractional-order Double-shell). The two proposed models represent the characterization of the biological cellular morphology of the plant stem. Experiments are conducted on two samples of three different medical plant species from the family Lamiaceae, and each sample is measured at two inter-electrode spacing distances. Bio-impedance measurements are done using an electrochemical station (SP150) in the range of 100 Hz to 100 kHz. All employed models are compared by fitting the measured data to verify the efficiency of the proposed models in modeling the plant stem tissue. The proposed models give the best results in all inter-electrode spacing distances. Four different metaheuristic optimization algorithms are used in the fitting process to extract all models parameter and find the best optimization algorithm in the bio-impedance problems.

Indexed as

AlgorithmsBiophysicsElectric ImpedanceElectrodesPlant Stems

Identifiers

PMID35273205
PMCPMC8913657

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.